Data & Insight Manager
Role Purpose
The Data & Insights Manager is responsible for building and operating a data product capability that enables scalable, reliable, and actionable insights across the organisation.
This role goes beyond traditional reporting and is accountable for bridging business needs with data platform capabilities, ensuring that data is structured, governed, and delivered as reusable products.
Key Responsibilities
Data Product Ownership & Roadmap
Define and own the data product roadmap
Translate business needs into scalable data solutions
Prioritise initiatives across BI, engineering, and architecture
Ensure delivery is aligned to measurable business outcomes
Team Leadership & Operating Model
Lead and develop a cross-functional data team (Analysts, Engineers, Architect, Scientist)
Establish and enforce a Data-as-a-Product operating model
Drive clarity of roles, responsibilities, and ways of working
Stakeholder Management
Act as primary interface between data and business stakeholders
Align Commercial, Product, Finance, Risk, Operations and Technology on data priorities
Drive adoption of data products and insights
Data Governance & Quality
Define and enforce data ownership and accountability
Ensure consistency of key business metrics
Establish data quality and trust frameworks
Delivery & Execution
Own prioritisation and backlog management
Ensure timely and high-quality delivery of data products
Balance short-term reporting needs with long-term platform investments
Required Skills & Experience
5–10+ years experience in data, analytics, or data product roles, with at least 3+ years in a leadership or management capacity
Proven experience owning end-to-end delivery of data initiatives, from requirements through to adoption
Experience managing or leading cross-functional data teams (e.g. analysts, engineers)
Strong data product mindset, with experience delivering scalable, reusable data solutions (not just reports or dashboards)
Solid understanding of:
Data pipelines (ETL/ELT concepts)
Data modelling (e.g. fact/dimension, semantic layers)
Data warehousing / lakehouse concepts
Experience working closely with Data Engineering and Architecture teams
Strong stakeholder management skills, with the ability to influence across business and technology teams
Ability to translate complex data into clear, actionable business insights
Proven ability to prioritise and balance short-term delivery vs long-term platform investment
Comfortable operating in fast-paced, evolving environments with ambiguity
Nice to Have
Experience building or operating in a Data-as-a-Product environment
Experience in iGaming, fintech, or other transactional platforms
Familiarity with modern data stack tools (e.g. Snowflake, BigQuery, dbt, Airflow)
Experience with API-driven or event-based systems
Exposure to real-time or near real-time data processing
Experience supporting or enabling AI/ML initiatives
Background in both technical and business-facing roles
Success Metrics
Adoption and usage of data products
Improved speed and quality of decision-making
Data consistency and trust across the organisation
Delivery against roadmap and business impact
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